A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object classification using heterogeneous co-occurrence features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Recursive coarse-to-fine localization for fast object detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Spatial Recurrences for Pedestrian Classification
Journal of Mathematical Imaging and Vision
| Hi-index | 0.00 |
Recent work on pedestrian detection has relied on the concept of local co-occurences of features to propose higher-order, richer descriptors. While this idea has proven to be benefitial for this detection task, it fails to properly account for a more general and/or holistic representation. In this paper, a novel, flexible, and modular descriptor is proposed which is based on the alternative concept of visual recurrence and, in particular, on a mathematically sound tool, the recurrence plot. The experimental work conducted provides evidence on the discriminatory power of the descriptor, with results comparable to recent similar approaches. Furthermore, since its degree of locality, its visual compactness, and the pair-wise feature similarity can be easily changed, it holds promise to account for characterizations of other descriptors, as well as for a range of accuracy-computational trade-offs for pedestrian detection and, possibly, also for other object detection problems.